Data mining ‐ a powerful information creating tool

Abstract

Data mining can discover information hidden within valuable data assets. Knowledge discovery, using advanced information technologies, can uncover veins of surprising, golden insights in a mountain of factual data. Data mining consists of a panoply of powerful tools which are intuitive, easy to explain, understandable, and simple to use. These advanced information technologies include artificial intelligence methods (e.g. expert systems, fuzzy logic, etc.), decision trees, rule induction methods, genetic algorithms and genetic programming, neural networks (e.g. backpropagation, associate memories, etc.), and clustering techniques. The synergy created between data warehousing and data mining allows knowledge seekers to leverage their massive data assets, thus improving the quality and effectiveness of their decisions. The growing requirements for data mining and real time analysis of information will be a driving force in the development of new data warehouse architectures and methods and, conversely, the development of new data mining methods and applications.

Mining students’ behavior in web-based learning programs

“…It is a systematic approach for exploring underlying patterns, trend, and relationships hidden in available data (Lee, Chiu, Chou, & Lu, 2006), and it can be used to generate rules to predict correlations (Gargano & Ragged, 1999). One of the advantages of using data mining techniques is that unexpected relationships within a dataset can be discovered (Chang & Chen, 2005;Waite, Wheeler, & Bromfield, 2007).…”

“…Unlike most of previous studies, which used traditional statistical methods to analyze students' learning behavior, answers to this research question are sought by using a data mining technique to conduct data analyses. This is due to the fact that data mining can uncover hidden relationships, patterns, and interdependencies (Gargano & Ragged, 1999;Hedberg, 1995).…”

A manufacturing model to enable knowledge maintenance in decision support systems

“…DM systems can discover various types of knowledge, including association rules, characteristics rules, classification rules, clustering, evolution, and deviation analysis (Lee and Siau 2001). Using advanced information technologies, KDD can uncover veins of surprising and golden insights in a mountain of factual data (Gargano and Raggad 1999). Although the knowledge can be generated by DM tools, the clear definition of the knowledge elements depicted in Figure 2-6 is necessary to maximise the effectiveness of the KM process (Shaw et al 2001) (Metaxiotis et al 2003) Different researchers are used eXtensible Markup Language (XML) to create software tools to support KM and knowledge discovery.…”

A stage-based model for enabling decision support in process mining

“…In Gargano and Raggad's opinion [34],"Using advanced information technologies, knowledge discovery in databases (KDD) can uncover veins of surprising and golden insights in a mountain of factual data". As shown by Below [9], the extraction of useful knowledge from data and its subsequent deployment for a constructive purpose are the key aims of data mining.…”

“…"Data mining is the process of discovering interesting patterns from massive amounts of data. As a knowledge discovery process, it typically involves data cleaning, data integration, data selection, data transformation, pattern discovery, pattern evaluation and knowledge presentation" [34]. Tiwari et al [85] state that data mining works by extracting knowledge through identifying the patterns from huge data sets.…”